Key result
Lower wearable sampling rates and shorter signal lengths underestimate stride-interval α versus higher settings.
Why the study?
Methodological considerations for translating wearable-based gait-monitoring data to field settings for athlete fatigue management were not well established.
Observational (n=23)
Effect estimate: MD -0.02 to -0.08
p-value: p=≤0.045
Detecting altered gait following intensive training is possible using 200 to 300 strides and a 100-Hz sampling rate, though higher sampling rates may be more accurate.
Lower sampling rates or short signals risk underestimating stride-interval α; optimal protocols for detecting training-related gait changes remain undefined.
PURPOSE: There are important methodological considerations for translating wearable-based gait-monitoring data to field settings. This study investigated different devices' sampling rates, signal lengths, and testing frequencies for athlete monitoring using dynamical systems variables. METHODS: Secondary analysis of previous wearables data (N = 10 runners) from a 5-week intensive training intervention investigated impacts of sampling rate (100-2000 Hz) and signal length (100-300 strides) on detection of gait changes caused by intensive training. Primary analysis of data from 13 separate runners during 1 week of field-based testing determined day-to-day stability of outcomes using single-session data and mean data from 2 sessions. Stride-interval long-range correlation coefficient α from detrended fluctuation analysis was the gait outcome variable. RESULTS: Stride-interval α reduced at 100- and 200- versus 300- to 2000-Hz sampling rates (mean difference: -.02 to -.08; P ≤ .045) and at 100- compared to 200- to 300-stride signal lengths (mean difference: -.05 to -.07; P < .010). Effects of intensive training were detected at 100, 200, and 400 to 2000 Hz (P ≤ .043) but not 300 Hz (P = .069). Within-athlete α variability was lower using 2-session mean versus single-session data (smallest detectable change: .13 and .22, respectively). CONCLUSIONS: Detecting altered gait following intensive training was possible using 200 to 300 strides and a 100-Hz sampling rate, although 100 and 200 Hz underestimated α compared to higher rates. Using 2-session mean data lowers smallest detectable change values by nearly half compared to single-session data. Coaches, runners, and researchers can use these findings to integrate wearable-device gait monitoring into practice using dynamic systems variables.
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Fuller et al. (2023) conducted an observational in Fatigue management in runners (n=23). Lower sampling rates (100-200 Hz) and shorter signal lengths (100 strides) vs. Higher sampling rates (300-2000 Hz) and longer signal lengths (200-300 strides) was evaluated on Stride-interval long-range correlation coefficient α (MD -0.02 to -0.08, p=≤0.045). Lower sampling rates (100-200 Hz) and shorter signal lengths (100 strides) underestimated stride-interval α compared to higher rates and longer lengths (P≤0.045 and P<0.010, respectively).
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